Machine learning is a discipline that explores the design of algorithms that can learn from data. Machine learning algorithms may be adapted to inputs to build a model, and can then be used on new data to make predictions. Machine learning has ties to statistics, artificial intelligence and optimization, and is often employed in tasks where explicit rule-based algorithms are difficult to formulate. Examples of such tasks include image recognition, character recognition and email spam filtering.
Artificial neural networks are computational tools capable of machine learning. In artificial neural networks, which may be referred to as neural networks hereinafter, interconnected computation units known as “neurons” are allowed to adapt to training data, and subsequently work together to produce predictions in a model that to some extent may resemble processing in biological neural networks.
Neural networks may comprise a set of layers, the first one being an input layer configured to receive an input. The input layer comprises neurons that are connected to neurons comprised in a second layer, which may be referred to as a hidden layer. Neurons of the hidden layer may be connected to a further hidden layer, or an output layer.
In some neural networks, each neuron of a layer has a connection to each neuron in a following layer. Such neural networks are known as fully connected networks. The training data is used to let each connection to assume a weight that characterizes a strength of the connection. Some neural networks comprise both fully connected layers and layers that are not fully connected. Fully connected layers in a convolutional neural network may be referred to as densely connected layers.
In some neural networks, signals propagate from the input layer to the output layer strictly in one way, meaning that no connections exist that propagate back toward the input layer. Such neural networks are known as feed forward neural networks. In case connections propagating back toward the input layer do exist, the neural network in question may be referred to as a recurrent neural network.
Convolutional neural networks, CNN, are feed-forward neural networks that comprise layers that are not fully connected. In CNNs, neurons in a convolutional layer are connected to neurons in a subset, or neighbourhood, of an earlier layer. This enables, in at least some CNNs, retaining spatial features in the input. CNNs may have both convolutional and fully connected layers.